Literature DB >> 22361665

Artifact removal in physiological signals--practices and possibilities.

Kevin T Sweeney1, Tomás E Ward, Seán F McLoone.   

Abstract

The combination of reducing birth rate and increasing life expectancy continues to drive the demographic shift toward an aging population. This, in turn, places an ever-increasing burden on healthcare due to the increasing prevalence of patients with chronic illnesses and the reducing income-generating population base needed to sustain them. The need to urgently address this healthcare "time bomb" has accelerated the growth in ubiquitous, pervasive, distributed healthcare technologies. The current move from hospital-centric healthcare toward in-home health assessment is aimed at alleviating the burden on healthcare professionals, the health care system and caregivers. This shift will also further increase the comfort for the patient. Advances in signal acquisition, data storage and communication provide for the collection of reliable and useful in-home physiological data. Artifacts, arising from environmental, experimental and physiological factors, degrade signal quality and render the affected part of the signal useless. The magnitude and frequency of these artifacts significantly increases when data collection is moved from the clinic into the home. Signal processing advances have brought about significant improvement in artifact removal over the past few years. This paper reviews the physiological signals most likely to be recorded in the home, documenting the artifacts which occur most frequently and which have the largest degrading effect. A detailed analysis of current artifact removal techniques will then be presented. An evaluation of the advantages and disadvantages of each of the proposed artifact detection and removal techniques, with particular application to the personal healthcare domain, is provided.

Entities:  

Mesh:

Year:  2012        PMID: 22361665     DOI: 10.1109/TITB.2012.2188536

Source DB:  PubMed          Journal:  IEEE Trans Inf Technol Biomed        ISSN: 1089-7771


  29 in total

1.  Sensor fusion methods for reducing false alarms in heart rate monitoring.

Authors:  Gabriel Borges; Valner Brusamarello
Journal:  J Clin Monit Comput       Date:  2015-10-06       Impact factor: 2.502

2.  Single Channel EEG Artifact Identification Using Two-Dimensional Multi-Resolution Analysis.

Authors:  Mojtaba Taherisadr; Omid Dehzangi; Hossein Parsaei
Journal:  Sensors (Basel)       Date:  2017-12-13       Impact factor: 3.576

3.  ABOT: an open-source online benchmarking tool for machine learning-based artefact detection and removal methods from neuronal signals.

Authors:  Marcos Fabietti; Mufti Mahmud; Ahmad Lotfi; M Shamim Kaiser
Journal:  Brain Inform       Date:  2022-09-01

4.  Gaussian Elimination-Based Novel Canonical Correlation Analysis Method for EEG Motion Artifact Removal.

Authors:  Vandana Roy; Shailja Shukla; Piyush Kumar Shukla; Paresh Rawat
Journal:  J Healthc Eng       Date:  2017-10-08       Impact factor: 2.682

Review 5.  Personalizing neuromodulation.

Authors:  John D Medaglia; Brian Erickson; Jared Zimmerman; Apoorva Kelkar
Journal:  Int J Psychophysiol       Date:  2019-01-24       Impact factor: 2.997

6.  Iterative Covariance-Based Removal of Time-Synchronous Artifacts: Application to Gastrointestinal Electrical Recordings.

Authors:  Jonathan C Erickson; Joy Putney; Douglas Hilbert; Niranchan Paskaranandavadivel; Leo K Cheng; Greg O'Grady; Timothy R Angeli
Journal:  IEEE Trans Biomed Eng       Date:  2016-01-26       Impact factor: 4.538

7.  Massage Therapy's Effectiveness on the Decoding EEG Rhythms of Left/Right Motor Imagery and Motion Execution in Patients With Skeletal Muscle Pain.

Authors:  Huihui Li; Kai Fan; Junsong Ma; Bo Wang; Xiaohao Qiao; Yan Yan; Wenjing Du; Lei Wang
Journal:  IEEE J Transl Eng Health Med       Date:  2021-02-03       Impact factor: 3.316

8.  UnoViS: the MedIT public unobtrusive vital signs database.

Authors:  Tobias Wartzek; Michael Czaplik; Christoph Hoog Antink; Benjamin Eilebrecht; Rafael Walocha; Steffen Leonhardt
Journal:  Health Inf Sci Syst       Date:  2015-06-02

Review 9.  A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal.

Authors:  Sani Saminu; Guizhi Xu; Zhang Shuai; Isselmou Abd El Kader; Adamu Halilu Jabire; Yusuf Kola Ahmed; Ibrahim Abdullahi Karaye; Isah Salim Ahmad
Journal:  Brain Sci       Date:  2021-05-20

10.  An exploratory data quality analysis of time series physiologic signals using a large-scale intensive care unit database.

Authors:  Ali S Afshar; Yijun Li; Zixu Chen; Yuxuan Chen; Jae Hun Lee; Darius Irani; Aidan Crank; Digvijay Singh; Michael Kanter; Nauder Faraday; Hadi Kharrazi
Journal:  JAMIA Open       Date:  2021-08-02
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.